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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # 2027 PhD Graduate - Artificial Intelligence and Complex Systems - **Company:** The Johns Hopkins University Applied Physics Laboratory LLC - **Location:** North Laurel, MD, United States - **Experience:** Starter - **Salary:** $105,000.0 - $245,000.0 - **Contract:** Contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Cloud Computing, Distributed Systems, Python (Programming Language), Machine Learning, High Performance Computing, Pytorch, Large Language Models, Deep Learning, Information Technology - **Published:** September 16, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18314526?backUrl=%2Fcareer%2F18314526%2F2027-Phd-Graduate-Artificial-Intelligence-Complex-Systems-Maryland-Laurel ## About the Role Are you eager to use artificial intelligence (AI) to unlock insights from complex, high-dimensional data towards national security impact? Do you want to bring innovative AI to real-world challenges in domains such as materials science, biology, chemistry, and advanced manufacturing? Are you pursuing studies in AI, machine learning (ML), or a related field, and looking for a role where you can push the boundaries of AI for Science in interdisciplinary, high-impact ways?, * Have a PhD degree in a relevant field such as AI, Mathematics, Statistics, Computer Science, Data Science, or Engineering. * Have published previous research in peer-reviewed journals and/or conferences. * Have experience in one or more key technical skills, including modern AI/ML methods, deep learning, foundation models, statistical modeling, agentic workflows, physics- or domain-informed ML, AI-driven surrogate modeling, diffusion modeling, neural operators, graph neural networks, large language models, or symbolic AI. * Are proficient in Python programming and have experience developing AI/ML methods using a modern deep learning framework (e.g., PyTorch, JAX). * Have experience with containerized, cloud, High Performance Computing (HPC), or distributed computing environments. * Are able to obtain an Interim Secret security clearance by your start date and can ultimately obtain a Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. You'll go above and beyond our minimum requirements if you... * Have background knowledge and demonstrated experience in a science or engineering discipline such as materials science, microbiology, chemistry, earth systems, and other physical and biological systems. * Are skilled in project management and/or leading technical teams. * Have experience writing technical proposals, particularly for government research sponsors. * Have experience applying AI/ML to real-world problems involving remote sensing, molecular, chemical, materials, protein engineering, omics, additive manufacturing, and other scientific data modalities. ## Description We are seeking a creative problem solver with strong technical capabilities and initiative to join our team in taking on challenges in complex systems. The Complex Systems Group, part of the Intelligent Systems Center (www.jhuapl.edu/isc), conducts research at the intersection of AI and complex systems, with an emphasis on advancing capabilities in machine learning-based surrogate models, scientific automation, AI-assisted reasoning, and AI-guided design. We develop methods to drive discovery and design across scientific and engineering domains towards national security impact. This may include AI research in materials science, microbiology, chemistry, earth systems, and other physical and biological systems. As an AI researcher in our group, your primary responsibility will be contributing to and leading AI for Science projects with national security implications. As a member of our research team, you will contribute to AI for Science projects by creating, adapting, and evaluating modern AI methods including machine learning models, frontier AI systems, agentic workflows, physics- and domain-informed learning, AI-driven surrogate models, and closed-loop discovery architectures for scientific and engineering problems. You will establish collaborations with science and engineering domain experts and seek opportunities to propose your ideas for funding. You will also establish working relationships with our Program Managers to identify opportunities to grow our work in key areas for our sponsors. 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